How does the Second Opinion skill work?

It monitors your conversation for complex technical decisions or specific trigger words and suggests running commands like /codex or /gemini to get additional perspectives from other AI models.

When should I use this skill instead of just asking Claude?

Use it when facing complex trade-offs, security-critical implementations, or when you want to ensure your architectural approach aligns with industry best practices across multiple AI training sets.

Can I use this for private or sensitive code?

Yes, the skill specifically suggests using /ollama for sensitive code, which allows you to run local models that keep your proprietary data on your own machine.

What models are supported for a second opinion?

The skill facilitates integration with various LLM tools including OpenAI (via /codex), Google Gemini, and local models through the Ollama integration.

Second Opinion Multi-Model Assistant

Name: Second Opinion Multi-Model Assistant
Author: gopherguides

bygopherguides

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协作工具

Proactively suggests alternative LLM perspectives for complex architectural decisions, security reviews, and technical trade-offs.

关于

The Second Opinion skill enhances Claude Code by identifying moments of high ambiguity or criticality where a single AI perspective may not be enough. It monitors for architectural debates, security-sensitive implementations, and complex logic, offering to invoke external models like OpenAI Codex, Google Gemini, or local Ollama instances. This ensures more robust code quality and helps developers avoid tunnel vision by comparing trade-offs across different AI reasoning engines, ultimately leading to more informed and resilient software design.

主要功能

Provides multi-model comparisons to evaluate diverse technical approaches
Supports local model integration via Ollama for privacy-focused reviews
3 GitHub stars
Recognizes natural language 'sanity check' requests to offer timely cross-validation
Triggers security-specific suggestions for authentication and sensitive logic
Identifies architectural design pattern trade-offs and 'forks-in-the-road'

使用场景

Resolving contentious pattern debates by comparing how different LLMs weigh performance vs. readability
Validating high-stakes architectural choices like Microservices vs. Monolith designs
Conducting secondary security audits on sensitive crypto or PII-handling code

关于

主要功能

Provides multi-model comparisons to evaluate diverse technical approaches
Supports local model integration via Ollama for privacy-focused reviews
3 GitHub stars
Recognizes natural language 'sanity check' requests to offer timely cross-validation
Triggers security-specific suggestions for authentication and sensitive logic
Identifies architectural design pattern trade-offs and 'forks-in-the-road'

使用场景

Resolving contentious pattern debates by comparing how different LLMs weigh performance vs. readability
Validating high-stakes architectural choices like Microservices vs. Monolith designs
Conducting secondary security audits on sensitive crypto or PII-handling code